The ability to detect and isolate component faults in a railway suspension system is important for improved train safety and maintenance. An undetected failure in the suspension systems can cause severe wheel-rail wear, reduce ride comfort, worsen passenger safety and increase unexpected maintenance costs. Existing fault detection methods are limited in several respects, such as effectiveness/sensitivity for fault detection, or robustness to external condition changes. This thesis investigates a model-less fault detection and isolation approach using cross correlation and/or relative variance techniques, developed to overcome these limitations. This thesis treats a conventional bogie vehicle with a symmetrical structure. Excited by the track irregularities, the dynamics of the vehicle are studied under the normal conditions, with an emphasis on the vertical and related motions of the bogies and the carbody. Two fault detection schemes employing data processing using data directly from measurement are discussed. One uses cross correlation evaluation of the basic bogie motions to detect component fault; the other takes advantage of the relationship between the relative variances of the suspension accelerations. Finally, the fault isolation schemes are assessed based on the comparison of fault detection performances in different conditions. The proposed approach does not require detailed knowledge of the vehiclelbogie and external track irregularities. The effectiveness of the approach is verified by computer simulations in Matlab/Simulink.